Canadian RAG implementation that fixes retrieval quality first, because that's what fails.
One Canadian team builds the RAG pipeline and measures whether it actually retrieves.
Structure-aware splitting, so a policy clause doesn't get cut across two chunks. Metadata travels with every chunk into the embeddings pipeline, because a passage with no source, no date, and no owner can't be cited or filtered later.
Hybrid search pairs semantic search with keyword matching, so part numbers and policy codes survive alongside meaning. A reranker orders what comes back, permission filters apply at query time, and the vector database is a decision we make late, once your data volume and residency needs are known.
Context recall and precision measured on real traffic, not a curated demo set. Change the chunking, watch the number move, keep it or revert it. That's the difference between a system and an opinion.
When RAG fails, it's retrieval roughly three quarters of the time. Most projects measure the model instead, so they tune prompts while the defect sits one layer down in the rag pipeline.
IN PRODUCTION
The first version demoed beautifully on twenty curated documents and fell apart on forty thousand real ones. SMEnode rebuilt the chunking around our document structure, added a reranker, and put an eval loop on live queries. The useful part was the number: they could tell us retrieval recall went from 0.41 to 0.88, so we stopped arguing about whether it felt better.
Nearly every failed RAG project failed at retrieval. Almost none of them measured it.
Retrieval augmented generation isn't hard to demo and is hard to run. The demo works because twenty documents were hand-picked. Production breaks because forty thousand weren't. When RAG fails, retrieval is the failure point 73% of the time, not generation, and roughly 70% of teams running it in production have no systematic evaluation of retrieval quality at all (source: industry analyses, 2026). So they tune prompts and swap models while the actual defect sits one layer down, in what the system handed the model to read.
The pipeline has parts and each one can break it. Risk sits in three places. Chunking by token count, which splits a clause from its condition and hands the model half a rule. A single dense vector search with no reranking and no keyword fallback, so exact terms like part numbers and policy codes get missed. And retrieval that ignores who's asking, which turns your knowledge base into a data leak wearing a helpful interface. A RAG architecture we build has hybrid retrieval, structure-aware chunking, a reranker, and permission filters at query time.
Canadian context makes the permission layer non-negotiable. 19.2% of Canadian businesses now use AI in production, up from 12.2% a year earlier (source: Statistics Canada, 2026-05-27), and PIPEDA applies to every document your pipeline can reach. A RAG system inherits your existing access model exactly, so a knowledge base built over an estate with broad sharing will surface things nobody intended. Data residency matters too, and we can keep the vector database and the documents in Canada.
Four steps, and the third one is where most engagements get honest. We build an evaluation set from your real questions before writing pipeline code, because without it whether the thing works becomes a matter of opinion and the loudest opinion wins. Once recall and precision are measurable, decisions get easy: change the chunking, see the number move, keep or revert. That loop is unglamorous and it's the entire difference between a demo and a system.
Document structure, formats, update frequency, and who's allowed to see what. Permissions before pipelines. If your access model is already too broad, we say so here rather than three months into a build.
Structure-aware chunking with metadata, embeddings, a vector store sized to your corpus, hybrid retrieval, reranking, and generation that cites its sources. Every stage is testable on its own, so a regression has an address.
Context recall and precision scored against your evaluation set. We report both numbers before you see a demo, because a demo that looks good on curated questions tells you nothing about the other thirty-nine thousand documents.
Ongoing scoring on live traffic, so quality drift shows up as a number instead of a complaint. When your documents change, the score moves first and someone can act on it before users lose trust in the answers.
Answers first, including when you shouldn't build one. An architect takes the call.
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